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Jiahong Li

6 accepted papers

2025

SSPNet: Leveraging Robust Medication Recommendation with History and Knowledge

IJCAI 2025

Automated medication recommendation is a crucial task within the domain of artificial intelligence in healthcare, where recommender systems are supposed to deliver precise, personalized drug combinations tailored to the evolving health states of patients. Existing approaches often treat clinical rec

2022

Domain Generalization via Shuffled Style Assembly for Face Anti-Spoofing

CVPR 2022poster

With diverse presentation attacks emerging continually, generalizable face anti-spoofing (FAS) has drawn growing attention. Most existing methods implement domain generalization (DG) on the complete representations. However, different image statistics may have unique properties for the FAS tasks. In…

Cited by 203PDFcodeScholar
2021

A Bilingual, OpenWorld Video Text Dataset and End-to-end Video Text Spotter with Transformer

NeurIPS 2021poster

Most existing video text spotting benchmarks focus on evaluating a single language and scenario with limited data. In this work, we introduce a large-scale, Bilingual, Open World Video text benchmark dataset(BOVText). There are four features for BOVText. Firstly, we provide 1,850+ videos with more t…

Cited by 35SourcecodeScholar
2021

Dynamic Inconsistency-aware DeepFake Video Detection

IJCAI 2021poster

The spread of DeepFake videos causes a serious threat to information security, calling for effective detection methods to distinguish them. However, the performance of recent frame-based detection methods become limited due to their ignorance of the inter-frame inconsistency of fake videos. In this…

Cited by 0SourcePDFScholar
2021

Frequency-Aware Discriminative Feature Learning Supervised by Single-Center Loss for Face Forgery Detection

CVPR 2021poster

Face forgery detection is raising ever-increasing interest in computer vision since facial manipulation technologies cause serious worries. Though recent works have reached sound achievements, there are still unignorable problems: a) learned features supervised by softmax loss are separable but not…

Cited by 333PDFScholar